Machine Learning Engineer, Platform

Brain Co.
San Francisco, New York
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Problem-solving","Reasoning","Technical ownership","Platform thinking","Measurement mindset"]

Build core ML capabilities for an AI-native platform used by multiple institutional product teams. Improve document extraction, strengthen a blueprint foundation model, and ship better improvement loops that let deployed systems learn from corrections. Work end-to-end on shared capabilities like extraction agents, unified evaluation, model routing, and continuous improvement machinery—turning research-frontier techniques (LLMs, RL fine-tuning, agentic systems) into production-ready systems with measurable customer impact.

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FursaFursa
Brain Co.
Brain Co.
2 weeks ago

Machine Learning Engineer, Platform

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Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live

Job Summary

Build core ML capabilities for an AI-native platform used by multiple institutional product teams. Improve document extraction, strengthen a blueprint foundation model, and ship better improvement loops that let deployed systems learn from corrections. Work end-to-end on shared capabilities like extraction agents, unified evaluation, model routing, and continuous improvement machinery—turning research-frontier techniques (LLMs, RL fine-tuning, agentic systems) into production-ready systems with measurable customer impact.
Location: San Francisco, New York
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Turn pod needs into platform capabilities by extracting general patterns without over-abstracting early.
  • •Own ML capabilities end-to-end with no handoff, maintaining behavior in production across deployments.
  • •Convert LLMs, RL fine-tuning, and agentic systems into capabilities that power many institutional workflows.
  • •Serve both project pods and domain experts as customers for the capabilities, aligning outputs with real decisions and deadlines.
  • •Engineer for production reality—accuracy, latency, cost, and reliability—then raise the company bar by sharing learnings across pods.

Key Requirements

  • •Understand how machine learning works, including loss functions, generalization under distribution shift, and evaluation failures in real systems.
  • •Hands-on experience with frontier LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning.
  • •Know when different modeling approaches win (e.g., fine-tuned segmentation models vs VLMs vs rule engines) and how to compose them into more accurate systems.
  • •Have strong platform instincts: generalize responsibly from multiple teams’ needs and treat internal teams as real customers with deadlines and success metrics.
  • •Be comfortable inventing the problem, data, and definition of success at the same time, while building new capabilities end-to-end.
Experience:AIMachine learningLLMsAgentic systemsApplied AI
Skills:Problem-solvingReasoningTechnical ownershipPlatform thinkingMeasurement mindset
Tech Stack:LLMsLLM promptingFine-tuningTool useAgentic systemsReinforcement learning (RL) fine-tuningVLMSegmentation modelsVision modelsRule enginesFoundation modelModel routingUnified evaluationRetrievalRetrainingSafe redeployment

Company Brief

Brain Co.
Brain Co. builds agent-native operating systems for regulated institutions across government, health, insurance, finance, and enterprise. Its platform emphasizes secure deployment, workflow integration, and AI applications tailored to complex operational environments.
Industry: AI & Machine Learning
Growth: Growth Stage Startup
Headquarters: San Francisco, United States
Founded: 2026
WebsiteLinkedIn